New dissimilarity measures on picture fuzzy sets and applications - Le Thi Nhung

Journal of Computer Science and Cybernetics, V.34, N.3 (2018), 219–231 DOI 10.15625/1813-9663/34/3/13223 NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS AND APPLICATIONS∗ LE THI NHUNGa, NGUYEN VAN DINH, NGOC MINH CHAU, NGUYEN XUAN THAO Faculty of Information Technology, Vietnam National University of Agriculture altnhung@vnua.edu.vn
Abstract. The dissimilarity measures between fuzzy sets/intuitionistic fuzzy sets/picture fuzzy sets are studied and applied in various matters. In this paper, we propose some new dissimilarity measures on picture fuzzy sets. These new dissimilarity measures overcome the restrictions of all existing dissimilarity measures on picture fuzzy sets. After that, we apply these new measures to the pattern recognition problems. Finally, we introduce a multi-criteria decision making (MCDM) method that uses the new dissimilarity measures and apply them in the supplier selection problems. Keywords. Picture fuzzy set; Dissimilarity measure; MCDM. 1. INTRODUCTION The ranking of subjects is very important in the decision-making process. The ranking can be based on measures such as the similarity measures, the distance measures or dissi- milarity measures. In practical problems, fuzzy set and intuitionistic fuzzy set have been widely used [3, 9, 12, 18, 19, 21, 22]. The dissimilarity measures between them were also studied and applied in various matters [10, 14, 16, 17, 20, 23]. In 2014, Picture fuzzy set was introduced by Cuong [4]. It has three memberships: a degree of positive membership, a degree of negative membership, and a degree of neutral membership. Picture fuzzy set is a generality of fuzzy set [42] and intuitionistic fuzzy set [1]. Today, picture fuzzy set has been studied and applied widely in many fields [2, 6, 8, 11, 24, 25, 26, 37], especially in clustering problems [13, 15, 27, 28, 29, 32, 33, 31, 36]. Hoa et al. [13] used picture fuzzy sets to apply for Geographic Data Clustering. Thao and Dinh approxima- ted the picture fuzzy set on the crisp approximation spaces to give results as rough picture fuzzy sets and picture fuzzy topologies [30]. Dinh et al. investigated the picture fuzzy set database [35]. Cuong and Hai [5] studied some fuzzy logic operators for picture fuzzy sets. The cross-entropy and similarity measures on picture fuzzy sets were studied by Wei and applied in MCDM [38, 41, 39, 40]. As opposed to the similarity measures, the dissimilarity measures on picture fuzzy sets were first introduced by Dinh et al. in 2017 [7, 34]. But these ∗This paper is selected from the reports presented at the 11th National Conference on Fundamental and Applied Information Technology Research (FAIR’11), Thang Long University, 09 - 10/08/2018. c© 2018 Vietnam Academy of Science & Technology 220 LE THI NHUNG dissimilarity measures have certain restrictions (detail in Example 1, Section 3). To continue with the idea of the dissimilarity measures on picture fuzzy sets in practical applications, we propose some new dissimilarity measures to overcome the mentioned restrictions and apply them in practical problems (detail in Example 1 and Example 2, Section 3). In the similarity measure, if the value of the similarity measure between two objects is greater, the two objects are more likely to be identical. On the contrary, in the dissimilarity measure, if the value of the dissimilarity measure between two objects is smaller, the two objects are considered to be the same. In this paper, we introduce some new dissimilarity measures on picture fuzzy sets. The paper is organized as follows: the concept of picture fuzzy set is recalled in Section 2. The dissimilarity measures on PFS-sets are defined in Section 3. After that, we introduce an application of the dissimilarity measures between PFS-sets for the pattern recognition in Section 4. We also propose a multi-criteria decision making using new dissimilarity measures and apply this MCDM to select the supplier in Section 5. 2. BASIC NOTIONS Definition 1. (see [4]) Picture fuzzy set on a universe U is an object of the form A = {(u, µA(u), ηA(u), γA(u))|u ∈ U}, where µA is a membership function, ηA is neutral mem- bership function, γA is non-membership function of A and 0 ≤ µA(u) + ηA(u) + γA(u) ≤ 1 for all u ∈ U . Further, we denote by PFS(U) the collection of picture fuzzy sets on U with U = {(u, 1, 0, 0)|u ∈ U} and ∅ = {(u, 0, 0, 1)|u ∈ U} for all u ∈ U. For A,B ∈ PFS(U) and for all u ∈ U consider some algebraic operators for picture fuzzy sets as follows: + Union of A and B: A ∪B = {(u, µA∪B(u), ηA∪B(u), γA∪B(u))|u ∈ U}, where µA∪B(u) = max{µA(u), µB(u)}, ηA∪B(u) = min{ηA(u), ηB(u)} and γA∪B(u) = min{γA(u), γB(u)}. + Intersection of A and B: A ∩B = {(u, µA∩B(u), ηA∩B(u), γA∩B(u))|u ∈ U}, where µA∩B(u) = min{µA(u), µB(u)}, ηA∩B(u) = min{ηA(u), ηB(u)}, and γA∩B(u) = max{γA(u), γB(u)}. + Subset: A ⊂ B iff µA(u) ≤ µB(u), ηA(u) ≤ ηB(u) and γA(u) ≥ γB(u). 3. NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS In this section, we introduce concept of dissimilarity measure on picture fuzzy sets. Definition 2. A function DM : PFS(U) × PFS(U) → R is a dissimilarity measure on PFS-sets if it satisfies the following properties: NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS 221 + PF-Diss 1: 0 ≤ DM(A,B) ≤ 1; + PF-Diss 2: DM(A,B) = DM(B,A); + PF-Diss 3: DM(A,A) = 0; + PF-Diss 4: If A ⊂ B ⊂ C then DM(A,C) ≥ max{DM(A,B), DM(B,C)} for all A,B,C ∈ PFS(U). In [7, 34] Dinh et al. gave some dissimilarity measures on picture fuzzy sets as follows. Definition 3. [7, 34] Let U = {u1, u2, ..., un} be a universe set. Given two picture fuzzy sets A,B ∈ PFS(U). We define some dissimilarity measures on picture fuzzy sets as follows: DMC(A,B) = 1 3n n∑ i=1 [|SA(ui)− SB(ui)|+ |ηA(ui)− ηB(ui)|] (1) where SA(ui) = |µA(ui)− γA(ui)| and SB(ui) = |µB(ui)− γB(ui)|. DMH(A,B) = 1 3n n∑ i=1 [|µA(ui)− µB(ui)|+ |ηA(ui)− ηB(ui)|+ |γA(ui)− γB(ui)|] . (2) DML(A,B) = 1 5n n∑ i=1 [ |SA(ui)− SB(ui)|+ |µA(ui)− µB(ui)|+ |ηA(ui)− ηB(ui)|+ |γA(ui)− γB(ui)| ] . (3) DMO(A,B) = 1√ 3n n∑ i=1 [ |µA(ui)−µB(ui)|2 + |ηA(ui)−ηB(ui)|2 + |γA(ui)−γB(ui)|2 ] 1 2 . (4) These measures have a restriction, which is shown in the following example. Example 1. Assume that there are two patterns denoted by picture fuzzy sets on U = {u1, u2} as follows: Let A1 = {(u1, 0, 0, 0), (u2, 0.2, 0.2, 0.1)}, A2 = {(u1, 0, 0.1, 0.1), (u2, 0.1, 0.1, 0.1)} and B = {(u1, 0, 0.1, 0), (u2, 0, 0.3, 0.1)}. Question: Which class of pattern does B belong to? + Case 1: If using DMC(A,B) in eq.(1) then DMC(A1, B) = DMC(A2, B) = 0.066666667. + Case 2: If using DMH(A,B) in eq.(2) then DMH(A1, B) = DMH(A2, B) = 0.066666667. + Case 3: If using DML(A,B) in eq.(3) then DML(A1, B) = DML(A2, B) = 0.06. + Case 4: If using DMO(A,B) in eq.(4) then DMO(A1, B) = DMO(A2, B) = 0.132111922. We do not know which class of pattern B belongs to when using these dissimilarity measures. 222 LE THI NHUNG This drawback suggests us to improve the dissimilarity measure on picture fuzzy sets. Suppose U = {u1, u2, ..., un} is an universe set. For any A,B ∈ PFS(U), we denote RA(uj) = µA(uj)− γA(uj), RB(uj) = µB(uj)− γB(uj), SA(uj) = ηA(uj)− γA(uj), SB(uj) = ηB(uj)− γB(uj), and Dj(A,B) = |RA(uj)−RB(uj)|+ |SA(uj)− SB(uj)| 4 (5) for all j = 1, 2, ..., n. Definition 4. Let U = {u1, u2, . . . , un} be an universal set. For any A,B ∈ PFS(U) the dissimilarity measure DMN : PFS(U)× PFS(U)→ [0, 1] is defined by DMN (A,B) = 1 n n∑ j=1 Dj(A,B). (6) Theorem 1. Let U = {u1, u2, ..., un} be a universal set. For any A,B ∈ PFS(U), a function DMN : PFS(U)× PFS(U)→ R defined by DMN (A,B) = 1n ∑n j=1Dj(A,B) satisfies (i) 0 ≤ DMN (A,B) ≤ 1; (ii) DMN (A,B) = DMN (B,A); (iii) DMN (A,A) = 0; (iv) If A ⊂ B ⊂ C then DMN (A,C) ≥ max{DMN (A,B), DMN (B,C)} for all A,B,C ∈ PFS(U). Proof. (i) We have 0 ≤ RA(uj), RB(uj), SA(uj), SB(uj) ≤ 1. Hence, 0 ≤ Dj(A,B) ≤ 1. Therefore, from eq.(6) we have 0 ≤ DMN (A,B) ≤ 1. (ii) It is obvious. (iii) It is obvious. (iv) If A ⊂ B ⊂ C then µA(uj) ≤ µB(uj) ≤ µC(uj), ηA(uj) ≤ ηB(uj) ≤ ηC(uj) and γA(uj) ≥ γB(uj) ≥ γC(uj) for all uj ∈ U . So that, RA(uj) ≤ RB(uj) ≤ RC(uj) and SA(uj) ≤ SB(uj) ≤ SC(uj). Hence, |RC(uj)−RA(uj)| ≥ max{|RC(uj)−RB(uj)|, |RB(uj)−RA(uj)|} and |SC(uj)− SA(uj)| ≥ max{|SC(uj)− SB(uj)|, |SB(uj)− SA(uj)|}. Hence, DMN (A,C) ≥ max{DMN (A,B), DMN (B,C)}. It means PF-Diss 4 is satisfied.  Now, we assign to uj a weight ωj ∈ [0, 1] such that ∑n j=1 ωj = 1. We can define a new dissimilarity measure between two picture fuzzy sets as follows. Definition 5. Let U = {u1, u2, ..., un} be a universal set. For any A,B ∈ PFS(U), a dissimilarity measure DMωN : PFS(U)× PFS(U)→ [0, 1] is defined by DMωN (A,B) = n∑ j=1 ωjDj(A,B). (7) NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS 223 Definition 6. Let U = {u1, u2, ..., un} be a universal set. For any A,B ∈ PFS(U), a dissimilarity measure DMωP : PFS(U)× PFS(U)→ [0, 1] is defined by DMωP (A,B) = n∑ j=1 ωjD P j , (A,B) (8) where Dpj (A,B) = [|RA(uj)−RB(uj)|p + |SA(uj)− SB(uj)|p] 1 p 4 (9) for all j = 1, 2, ..., n; p ∈ N∗. Theorem 2. Let U = {u1, u2, ..., un} be a universe set. Then for any A,B ∈ PFS(U) DMωN (A,B) = n∑ j=1 ωjDj(A,B) and DMωP (A,B) = n∑ j=1 ωjD P j (A,B) are the dissimilarity measures on picture fuzzy sets. Proof. It is easy. Example 2. We consider the problem in Example 1. In that example, we cannot determine whether sample B belongs to the class of pattern A1 or A2 if we use the dissimilarity measures in expressions eq.(1), eq.(2), eq.(3) and eq.(4). Now, we consider this problem with the new dissimilarity measures in eq.(6) and eq.(8) with ω1 = ω2 = 0.5 and p = 2. + Using the dissimilarity measure in eq.(6), we have DMN (A1, B) = 0.05 and DMN (A2, B) = 0.0375. + Using the dissimilarity measure in eq.(8), we have DMωP (A1, B) = 0.04045 and DM ω P (A2, B) = 0.03018. We can easily see that using two new measures we can conclude that the sample B belongs to the class of pattern A2. 4. APPLYING THE PROPOSED DISSIMILARITY MEASURE IN PATTERN RECOGNITION In this section, we will give some examples using dissimilarity measures in the pattern recognition. Given for m patterns A1, A2, . . . , Am are picture fuzzy sets in the universal set U = {u1, u2, . . . , un}. If we have a sample B is also a picture fuzzy set on U . Question: Which class of pattern does B belong to? To answer this question, we practice the following steps: Step 1. Compute the dissimilarity measures DM(Ai, B) of Ai(i = 1, 2, . . . ,m) and B. 224 LE THI NHUNG Step 2. We put B to the class of pattern A∗, in which DM(A∗, B) = min{DM(Ai, B)|i = 1, 2, ...,m}. Example 3. Assume that there are two patterns denoted by picture fuzzy sets on U = {u1, u2, u3} as follows A1 = {(u1, 0.1, 0.1, 0.1), (u2, 0.1, 0.4, 0.3), (u3, 0.1, 0, 0.9)}, A2 = {(u1, 0.7, 0.1, 0.2), (u2, 0.1, 0.1, 0.8), (u3, 0.1, 0.1, 0.7)}. Now, there is a sample B = {(u1, 0.4, 0, 0.4), (u2, 0.6, 0.1, 0.2), (u3, 0.1, 0.1, 0.8)}. Question: Which class of pattern does B belong to? To answer this question, we consider the dissimilarity measures shown in eq.(6), eq.(8) with the weight vector ω = ( 1 3 , 1 3 , 1 3 ) + Applying the dissimilarity measure in eq.(6), we have DMN (A1, B) = 0.1417, DMN (A2, B) = 0.1667. It means that B belongs to the class of pattern A1. + Applying the dissimilarity measure in eq.(8) with p = 2, we have DMωP (A1, B) = 0.0982, DM ω P (A2, B) = 0.1741. It means that B belongs to the class of pattern A1. + Applying the dissimilarity measure in eq.(8) with p = 3, we have DMωP (A1, B) = 0.0935, DM ω P (A2, B) = 0.161. It means that B belongs to the class of pattern A1. Example 4. Assume that there are three patterns denoted by picture fuzzy sets on U = {u1, u2, u3} as follows A1 = {(u1, 0.5, 0, 0.4), (u2, 0.5, 0.2, 0.25), (u3, 0.1, 0, 0.9), (u4, 0.1, 0.1, 0.65)}, A2 = {(u1, 0.7, 0.1, 0.2), (u2, 0.1, 0.1, 0.8), (u3, 0.1, 0.1, 0.7), (u4, 0.4, 0.1, 0.5)}, A3 = {(u1, 0.6, 0.1, 0.2), (u2, 0.6, 0.2, 0.15), (u3, 0, 0.1, 0.9), (u4, 0.15, 0.2, 0.6)}. Now, there is a sample B = {(u1, 0.5, 0.1, 0.4), (u2, 0.6, 0.15, 0.2), (u3, 0.1, 0, 0.8), (u4, 0.1, 0.2, 0.6)}. Question: Which class of pattern does B belong to? Using the weight vector ω = ( 1 4 , 1 4 , 1 4 , 1 4 ) and eq.(6), eq.(8), then: + Applying the dissimilarity measure in eq.(6), we have DMN (A1, B) = 0.0375, DMN (A2, B) = 0.15, DMN (A3, B) = 0.0594. It means that B belongs to the class of pattern A1. NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS 225 + Applying the dissimilarity measure in eq.(8) with p = 2, we have DMωP (A1, B) = 0.06, DMωP (A2, B) = 0.303, DM ω P (A3, B) = 0.099. It means that B belongs to the class of pattern A1. + Applying the dissimilarity measure in eq.(8) with p = 3, we have DMωP (A1, B) = 0.073, DMωP (A2, B) = 0.3598, DM ω P (A3, B) = 0.1154. It means that B belongs to the class of pattern A1. Example 5. Assume that there are three patterns denoted by picture fuzzy sets on U = {u1, u2, u3, u4} as follows A1 = {(u1, 0.3, 0.4, 0.1), (u2, 0.3, 0.4, 0.1), (u3, 0.6, 0.1, 0.2), (u4, 0.6, 0.1, 0.2)}, A2 = {(u1, 0.4, 0.4, 0.1), (u2, 0.3, 0.2, 0.4), (u3, 0.6, 0.1, 0.3), (u4, 0.5, 0.2, 0.2)}, A3 = {(u1, 0.4, 0.4, 0.1), (u2, 0.3, 0.1, 0.3), (u3, 0.6, 0.1, 0.2), (u4, 0.5, 0.2, 0.1)}. Now, there is a sample B = {(u1, 0.35, 0.65, 0), (u2, 0.55, 0.35, 0.1), (u3, 0.65, 0.1, 0.1), (u4, 0.6, 0.15, 0.2)}. Question: Which class of pattern does B belong to? To answer this question, we consider the dissimilarity measures shown in eq.(6), eq.(7), and eq.(8) with the weight vector ω = (0.4, 0.3, 0.2, 0.1) + Applying the dissimilarity measure in eq.(6), we have DMN (A1, B) = 0.06875, DMN (A2, B) = 0.125, DMN (A3, B) = 0.10625. ⇒ DMN (A1, B) < DMN (A3, B) < DMN (A2, B). It means that B belongs to the class of pattern A1. + Applying the dissimilarity measure in eq.(7), we have DMωN (A1, B) = 0.08625, DM ω N (A2, B) = 0.14125, DM ω N (A3, B) = 0.12375. ⇒ DMωN (A1, B) < DMωN (A3, B) < DMωN (A2, B). It means that B belongs to the class of pattern A1. + Applying the dissimilarity measure in eq.(8) with p = 2, we have DMωP (A1, B) = 0.06746, DM ω P (A2, B) = 0.10744, DM ω P (A3, B) = 0.09585. ⇒ DMωP (A1, B) < DMωP (A3, B) < DMωP (A2, B). It means that B belongs to the class of pattern A1. 5. APPLICATION IN MULTI-CRITERIA DECISION MAKING In the MCDM problem, one has to find an optimal alternative from a set of alternatives A = {A1, A2, . . . , Am}. In this section, we introduce a method based on the new dissimilarity measures to solve a MCDM problem. 226 LE THI NHUNG Step 1. Determine the criteria set C = {C1, C2, . . . , Cn} for the MCDM. Step 2. Express each alternative Ai as a picture fuzzy set on the set C = {C1, C2, . . . , Cn}, Ai = {(Cj , µij , ηij , γij)|Cj ∈ C} for all i = 1, 2, . . . ,m. Step 3. We choose the best alternative Ab to be also a picture fuzzy set on the set C = {C1, C2, . . . , Cn}. Step 4. Determine the weight ωj of criteria Cj by considering Cj = {(Ai, µij , ηij , γij)|Ai ∈ A} as a picture fuzzy set on A = {A1, A2, . . . , Am}. Based on the union of picture fuzzy sets we propose a method to determine the weight ωj of criteria Cj(j = 1, 2, . . . , n) as follows: • We calculate dj = d1j + d2j + d3j (10) where d1j = max 1≤i≤m µij , d2j = min 1≤i≤m ηij , and d3j = min 1≤i≤m γij for all j = 1, 2, . . . , n. Then, A∗ = {(Cj , d1j , d2j , d3j)|Cj ∈ C}= m⋃ i=1 Ai and dj in the eq.(10) is referred to frequency of Cj(j = 1, 2, . . . , n) in A ∗. So that, we can determine the weight ωj of criteria Cj(j = 1, 2, . . . , n) based on fre- quency dj(j = 1, 2, ..., n). • Put ω (k) j = d (k) j∑n j=1 d (k) j (11) for all j = 1, 2, . . . , n; k = 0, 1, 2, . . . Note that, when k = 0 then we have the weight ωj = 1 n for all j = 1, 2, . . . , n. Step 5. Compute the dissimilarity measures DM(Ai, Ab) between Ai(i = 1, 2, . . . ,m) and Ab. Step 6. Rank the alternatives based on the dissimilarity measures as follows Ai ≺ Ak iff DM(Ai, Ab) < DM(Ak, Ab)(i, k = 1, 2, . . . ,m). Example 6. Consider a supplier section problem. Suppose a construction company wants to procure the material for their upcoming project. The company invites the tenders for procuring the required material. Given five suppliers are {A1, A2, A3, A4, A5}. To find an optimal supply, we apply the six steps for solving this MCDM problem as follows: Step 1. The company has fixed criteria for supplier selection: C1: quality of material; C2: price; C3: services; C4: delivery; C5: technical support if required; C6: behavior. NEW DISSIMILARITY MEASURES ON PICTURE FUZZY SETS 227 Step 2. Alternatives Ai is expressed as a picture fuzzy set on a criteria set {C1, C2, . . . , C6} in Table 1 and Table 2. Step 3. The best alternative Ab is Ab = {(Cj , 1, 0, 0)|j = 1, 2, 3, 4, 5, 6}. Step 4. Using the eq.(1), we get d1 = 0.85, d2 = 1, d3 = 0.9, d4 = 1, d5 = 0.95, d6 = 0.9. To calculate the weight ωj of criteria Cj(j = 1, 2, . . . , 6) we use the eq.(11): k = 0 we have the weight vector is ω0 = ( 1 6 , 1 6 , 1 6 , 1 6 , 1 6 , 1 6 ). k = 1 we have the weight vector is ω1 = (0.145, 0.171, 0.171, 0.171, 0.171, 0.171). k = 2 we have the weight vector is ω2 = (0.125, 0.175, 0.175, 0.175, 0.175, 0.175). Table 1. The picture fuzzy decision matrix for the supplier selection C1 C2 C3 C4 A1 (0.4, 0.05, 0.5) (0.1, 0.1, 0.8) (0.7, 0, 0.3) (0.6, 0.1, 0.2) A2 (0.7, 0.05, 0.2) (0.5, 0.1, 0.3) (0.3, 0.3, 0.4) (0.8, 0.05, 0.1) A3 (0.6, 0.2, 0.1) (0.7, 0, 0.3) (0.6, 0.1, 0.2) (0.4, 0.3, 0.1) A4 (0.5, 0.05, 0.4) (0.4, 0.2, 0.3) (0.8, 0.1, 0.1) (0.7, 0.05, 0.2) A5 (0.4, 0.3, 0.3) (0.1, 0.15, 0.7) (0.5, 0.25, 0.2) (0.9, 0, 0.1) Table 2. The picture fuzzy decision matrix for the supplier selection (cont.) C5 C6 A1 (0.5, 0.1, 0.4) (0.3, 0.2, 0.4) A2 (0.2, 0.1, 0.6) (0.4, 0, 0.5) A3 (0.3, 0.2, 0.4) (0.8, 0, 0.2) A4 (0.6, 0.25, 0.1) (0.7, 0.2, 0.1) A5 (0.8, 0.05, 0.1) (0.6, 0, 0.4) Step 5. Compute the dissimilarity measures DM(Ai, Ab) between Ai(i = 1, 2, . . . ,m) and Ab using the eq.(8) with p = 1 and p = 2. Step 6. Rank the alternatives based on the dissimilarity measure. The results of Step 5 and Step 6 with the various weight vectors are shown in Table 3, 4, 5. - With the weight vector ω0 = ( 1 6 , 1 6 , 1 6 , 1 6 , 1 6 , 1 6 ), we have the dissimilarity measure and ranking of alternatives as in Table 3. - With the weight vector ω1 = (0.145, 0.171, 0.171, 0.171, 0.171, 0.171), we have the dissimilarity measure and ranking of alternatives as in Table 4. 228 LE THI NHUNG Table 3. The dissimilarity measure and ranking of alternatives with the weight vector ω0 A1 A2 A3 A4 A5 p = 1 DM(Ai, Ab) 0.2688 0.2229 0.1833 0.1813 0.1917 Rank 5 4 2 1 3 p = 2 DM(Ai, Ab) 0.2651 0.2281 0.1776 0.1693 0.198 Rank 5 4 2 1 3 Table 4. The dissimilarity measure and ranking of alternatives with the weight vector ω1 A1 A2 A3 A4 A5 p = 1 DM(Ai, Ab) 0.2657 0.2245 0.1842 0.1778 0.1908 Rank 5 4 2 1 3 p = 2 DM(Ai, Ab) 0.2919 0.2548 0.1957 0.1829 0.216 Rank 5 4 2 1 3 - With the weight vector ω2 = (0.125, 0.175, 0.175, 0.175, 0.175, 0.175), we have the dissimilarity measure and ranking of alternatives as in Table 5. Table 5. The dissimilarity measure and ranking of alternatives with the weight vector ω2 A1 A2 A3 A4 A5 p = 1 DM(Ai, Ab) 0.2632 0.22261 0.1852 0.175 0.1902 Rank 5 4 2 1 3 p = 2 DM(Ai, Ab) 0.2902 0.257 0.197 0.1802 0.215 Rank 5 4 2 1 3 6. CONCLUSION In this paper, we introduce some new dissimilarity measures on picture fuzzy sets. 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